https://www.ias-iss.org/ojs/IAS/issue/feedImage Analysis & Stereology2021-07-09T13:45:30+00:00Marko Kreft, IAS Editor in chiefmarko.kreft@bf.uni-lj.siOpen Journal Systems<p>Image Analysis and Stereology is the official journal of the <a href="http://www.issia.net/">International Society for Stereology & Image Analysis</a>. It promotes the exchange of scientific, technical, organizational and other information on the quantitative analysis of data having a geometrical structure, including stereology, differential geometry, image analysis, image processing, mathematical morphology, stochastic geometry, statistics, pattern recognition, and related topics. The fields of application are not restricted and range from biomedicine, materials sciences and physics to geology and geography. Image Analysis & Stereology is a continuation of <a href="http://popups.ulg.ac.be/0351-580x/index.php?page=presentation">Acta Stereologica</a>.</p><p>This journal is regularly indexed or abstracted in: <a title="Cabell Publishing" href="http://www.cabells.com">Cabell Publishing</a>, <a href="http://www.csa.com/">Cambridge Scientific Abstracts</a>, <a href="http://www.cas.org/">Chemical Abstracts</a>, <a href="http://thomsonreuters.com/current-contents-connect/">Current Contents®/Engineering Computing and Technology</a>, <a href="http://www.statindex.org/">Current Index to Statistics (CIS)</a>, <a href="http://www.doaj.org/">DOAJ</a>, <a href="http://www.ebscohost.com/">EBSCO</a>, <a href="http://www.theiet.org/resources/inspec/">INSPEC</a>, <a title="New JCR titles in 2013" href="http://scientific.thomsonreuters.com/imgblast/JCR-newlist-2013.pdf">Journal Citation Reports/Science Edition</a>, <a href="http://www.ams.org/mr-database">Math Reviews</a>, <a href="http://www.ams.org/mathscinet">MATHSciNet</a>, <a href="http://www.medical-journals-links.com/biomathematics-biostatistics-biometrics-journals.php">Medical Journal Links</a>, <a href="http://search.proquest.com/metadex">METADEX</a>, <a href="http://en.wikipedia.org/wiki/Referativny_Zhurnal">Referativnyj Zhurnal</a>, Science Citation Index Expanded (SciSearch®), <a href="https://www.scopus.com/sourceid/11700154323">SCOPUS</a>, <a href="https://apps.webofknowledge.com/">Web of Knowledge</a> and <a href="http://www.emis.de/ZMATH/">Zentrallblatt MATH</a>.</p><p><span><span>Image Analysis & Stereology publishes</span><span class="m_-7660546945188024108apple-converted-space"> </span><a title="Abstracts in Slovenian" href="/ojs/IAS/pages/view/slovenian" target="_blank" data-saferedirecturl="https://www.google.com/url?hl=sl&q=https://www.ias-iss.org/default_002.html&source=gmail&ust=1487657459560000&usg=AFQjCNHSoETlTm9q9h97FDn1oJv3gBxkTA"><span>abstracts in Slovenian language</span></a><span>.</span></span></p><p><span>The JCR Impact Factor (SCI) for 2020 is <strong>0</strong></span><strong>.682</strong></p><p><strong></strong><a href="https://www.scopus.com/sourceid/11700154323?origin=resultslist" target="_blank">Scopus journal metrics</a>:</p><ul><li>CiteScore 2020 <strong>1.8</strong></li><li>SJR 2020 <strong>0.237</strong></li><li>SNIP 2020 <strong>0.661</strong></li></ul>https://www.ias-iss.org/ojs/IAS/article/view/2596Personal Reflections on the Life of Hans Jørgen Gottlieb Gundersen2021-07-09T13:45:30+00:00John F. Bertramjohn.bertram@monash.eduLuis M. Cruz-Oriveluis.cruz@unican.esStephen M. Evanssme64@me.comDallas M. Hydedmhyde@ucdavis.eduTerry MayhewTerry.Mayhew@nottingham.ac.ukMatthias Ochsmatthias.ochs@charite.deYong Tangytang062@163.comJens Randel Nyengaardjrnyengaard@clin.au.dk<p>Professor Hans Jørgen G. Gundersen MD, DMSc (1943–2021) was a pioneering stereologist whose work has inspired and influenced researchers across the world for almost half a century. He was a charismatic character and one of the founding fathers of modern stereology, whose achievements and contributions are fondly remembered below by colleagues and co-workers. It was an enormous pleasure to be in his company and although future generation will miss this opportunity, his work will live on, to inspire and influence future generations of researchers.</p>2021-07-09T13:34:16+00:00Copyright (c) 2021 Image Analysis & Stereologyhttps://www.ias-iss.org/ojs/IAS/article/view/2581Quantification of the Interventional Approaches Into the Pterygopalatine Fossa by Solid Angles Using Virtual Reality2021-07-09T13:45:29+00:00Anže Jermananze.jerman@gmail.comJiří JanáčekJiri.Janacek@fgu.cas.czŽiga Snojziga.snoj@gmail.comNejc Umeknejc.umek@mf.uni-lj.si<p>Virtual reality is increasingly used in medicine for diagnostics, for visualisation of complex structures and for preoperative planning. In interventional radiology, minimally invasive approach could be described with a target point representing the desired needle tip position and an array of all possible trajectories leading to it resembling irregular “cone” or “pyramid”. We present a pilot study of planning a minimally invasive posterior infrazygomatic and suprazygomatic approaches into the pterygopalatine fossa using a solid angle as a measure of size of the approach in five virtually reconstructed heads. The minimally invasive approaches were planned by manually drawing the edges of “pyramids” that described each approach in 3D using virtual reality program Tracer. For each head, a transverse diameter was measured and for each approach a solid angle size, average edge length and estimated area on the skin from where the target point could be reached were calculated. We found that, the solid angle of posterior infrazygomatic approach was significantly larger than suprazygomatic approach (<em>p</em><0.001). Furthermore, the transverse head diameter and solid angle in posterior infrazygomatic approach were negatively correlated (ρ=-0.55, <em>p</em>=0.0002), while transverse head diameter and the estimated area on the skin from where the target point could be reached in the suprazygomatic approach were positively correlated (ρ=0.37, <em>p</em>=0.0206). In conclusion, our findings provide important preliminary evidence on the feasibility of evaluating and comparing different minimally invasive approaches using virtual reality systems, and affirm the validity of solid angle as a measure of the size of the approach. </p>2021-07-09T13:34:16+00:00Copyright (c) 2021 Image Analysis & Stereologyhttps://www.ias-iss.org/ojs/IAS/article/view/2488Simulation of Large Aggregate Particles System With a New Morphological Model2021-07-09T13:45:29+00:00Maxime Moreaudmaxime.moreaud@ifpen.frGiulia Ferrigiulia.ferri@ifpen.frSeverine Humbertseverine.humbert@ifpen.frMathieu Dignemathieu.digne@ifpen.frJean-Marc Schweitzerjean-marc.schweitzer@ifpen.fr<p>For the development of a new porous material such as catalytic carrier, the control of the textural properties is of fundamental importance. In order to move towards rational synthesis, it is necessary to better understand the physical phenomena that generate a defined solid structure. A contribute to this purpose can be achieved by studying the aggregation process inside colloidal suspensions, leading to porosity generation: this phenomenon can be described with a Brownian dynamics model that, for any set of chemical parameters, gives access to the mass distribution and the fractal dimension of colloidal aggregates. However, this model cannot be used for the simulation of large colloidal systems, due to its high computational time, limiting comparison with analytical methods, which probe the whole multi-scale system. This problem is solved by developing a new aggregation morphological model, wherein the fractal dimension is tuned with two compactness parameters. An efficient simulation algorithm is proposed in case of spheres, for which the fractal dimension of the generated aggregates varies between 1.2 and 3. Brownian dynamics results are used to parametrize this purely geometric model, in order to constrain the size and the morphology of the aggregates created. The large numerical solid will be representative of the textural properties of a real solid and will give more information on the porous network. It could be used, for example, to simulate diffusive transport coupled with chemical reaction and to study the impact of the geometry of the porous system on the catalytic performance.</p>2021-07-09T13:34:16+00:00Copyright (c) 2021 Image Analysis & Stereologyhttps://www.ias-iss.org/ojs/IAS/article/view/2499Multidimensional Characterisation of Time-dependent Image Data: A Case Study for the Peripheral Nervous System in Ageing Mice2021-07-09T13:45:29+00:00Matthias Webermatthias.weber@uni-ulm.deThomas Wilhelmthomas.wilhelm@uni-ulm.deVolker Schmidtvolker.schmidt@uni-ulm.de<p>Segmentation of µm-resolution image data of irregularly shaped objects poses challenges to existing segmentation algorithms. This is especially true, when imperfections like noise, uneven lightning or traces of sample preparation are present in the image data. In this paper, considering electron micrographs of femoral quadriceps nerve sections of mice, a segmentation method to extract single axons surrounded by myelin sheaths is developed which is able to cope with various imperfections and artefacts. This approach successfully combines established methods like local thresholding and marker-based watershed transform to achieve a reliable segmentation of the given data. Indeed, the resulting segmentation map can be used to quantitatively determine geometrical characteristics of the axons and myelin sheaths. This is exemplified by modelling the joint probability distribution of axon area and myelin sphericity using a parametric copula approach, and by analysing the evolution of the model parameters for image data obtained from mice of different ages.</p>2021-07-09T13:34:16+00:00Copyright (c) 2021 Image Analysis & Stereologyhttps://www.ias-iss.org/ojs/IAS/article/view/2554Local Measures Distribution for the Estimation of the Elongation Ratio of the Typical Grain in Homogeneous Boolean Models2021-07-09T13:45:29+00:00Tatyana Ereminatatyana.eremina@emse.frJohan Debayledebayle@emse.frFrederic Gruyfgruy@emse.frJean-Charles Pinolipinoli@emse.fr<p>We introduce a particular localization of the Minkowski functionals to characterize and discriminate different random spatial structures. The aim of this paper is to present a method estimating the typical grain elongation ratio in a homogeneous Boolean model. The use of this method is demonstrated on a range of Boolean models of rectangles featuring fixed and random elongation ratio. An optimization algorithm is performed to determine the elongation ratio which maximize the likelihood function of the probability density associated with the local perimeter measure. Therefore, the elongation ratio of the typical grain can be deduced.</p>2021-07-09T13:34:16+00:00Copyright (c) 2021 Image Analysis & Stereologyhttps://www.ias-iss.org/ojs/IAS/article/view/2580A Novel Texture Descriptor: Circular Parts Local Binary Pattern2021-07-09T13:45:29+00:00Ibtissam Al Saidiibtissam_alsaidi@um5.ac.maMohammed Rzizamohammed.rziza@gmail.comJohan Debayledebayle@emse.fr<p>Local Binary Pattern (LBP) are considered as a classical descriptor for texture analysis, it has mostly been used in pattern recognition and computer vision applications. However, the LBP gets information from a restricted number of local neighbors which is not enough to describe texture information, and the other descriptors that get a large number of local neighbors suffer from a large dimensionality and consume much time. In this regard, we propose a novel descriptor for texture classification known as Circular Parts Local Binary Pattern (CPLBP) which is designed to enhance LBP by extending the area of neighborhood from one to a region of neighbors using polar coordinates that permit to capture more discriminating relationships that exists amongst the pixels in the local neighborhood which increase efficiency in extracting features. Firstly, the circle is divided into regions with a specific radius and angle. After that, we calculate the average gray-level value of each part. Finally, the value of the center pixel is compared with these average values. The relevance of the proposed idea is validate in databases Outex 10 and 12. A complete evaluation on benchmark data sets reveals CPLBP's high performance. CPLBP generates the score of 99.95 with SVM classification.</p>2021-07-09T13:34:16+00:00Copyright (c) 2021 Image Analysis & Stereology