bluuebunny/crossref_metadata_2025_split huggingface dataset:
Implementation of local structure tensor and enhancement anisotropic diffusion filters in ITK
Authors: Enquobahrie Andinet , Yang Hua , Aylward Stephen 
Published: January 2022

This paper describes implementation of local structure tensor and anisotropic enhancement diffusion filters using the Insight Toolkit. The anisotropic diffusion filters are implemented using ITK’s finite difference solver framework. The filters are used to implement the 3D edge-enhancing diffusion ( EED), coherence-enhancing diffusion (CED) and hybrid diffusion with continuous switch(HDCS) noise filtering algorithms described in Mendrik et al[1].The most up-to-date version of the code presented in this paper is distributed with the TubeTK project: http://public.kitware.com/Wiki/TubeTK
N4ITK: Nick's N3 ITK Implementation For MRI Bias Field Correction
Authors: Tustison Nicholas J. , Gee James 
Published: January 2022

Several algorithms exist for correcting the nonuniform intensity in magnetic resonance images caused by field inhomogeneities. These algorithms constitute important preprocessing steps for subsequent image analysis tasks. One such algorithm, known as parametric bias field correction (PABIC), has already been implemented in ITK. Another popular algorithm is the nonuniform intensity normalization (N3) approach. A particularly salient advantage of this algorithm is that it does not require a prior tissue model for its application. In addition, the source code for N3 is publicly available at the McConnell Brain Imaging Centre (Montreal Neurological Institute, McGill University) which includes source code and the coordinating set of perl scripts. This submission describes an implementation of the N3 algorithm for the Insight Toolkit given as a single class, viz. itk::N3MRIBiasFieldCorrectionImageFilter. We tried to maintain minimal difference between the publicly available MNI N3 implementation and our ITK im- plementation. The only intentional variation is the substitution of an earlier contribution, i.e. the class itk::BSplineScatteredDataPointSetToImageFilter, for the originally proposed least-squares approach for B-spline fitting used to model the bias field. In addition, we include a more extensive modification to the original N3 algorithm found in the class itk::N4MRIBiasFieldCorrectionImageFilter. The latter algorithm employs a multi-resolution approach, similar to FFD image registration strategies, and has a slightly modified iterative update scheme.
MIPP: A Medical Image Processing Platform based on ITK, VTK and Eclipse RCP
Authors: Swoboda Roland , Zwettler Gerald , Pfeifer Franz , Backfrieder Werner 
Published: January 2022

A novel platform for realizing software prototypes in the area of medical image processing, analysis and visualization is introduced: The "Medical Image Processing Platform" (MIPP). A novel feature is the combination of ITK/VTK with Java/Eclipse RCP, four potential technologies complementing each other. While ITK and VTK provide comprehensive functionality for image processing and visualization, Java and Eclipse RCP facilitate the development of a modern and rich application.RCP offers everything for creating plug-in based software. The benefits of integrating ITK and VTK in such a modular architecture are reusability and extensibility. Functionality usually found in software for medical image processing and visualization (2D and 3D viewers, DICOM and application data management, etc.) is provided by the MIPP platform as plug-ins, can be easily reused by new applications and needn't be re-implemented. Developers can extend the platform's features by contributing new plug-ins and, again, make these available for others.The paper describes the architecture of the MIPP platform and outlines its most important functionality. Finally, three clinical software systems implemented with MIPP are presented.
New Expectation Maximization Segmentation Pipeline in Slicer 3
Authors: Rannou Nicolas , Jaume Sylvain , Pieper Steve , Kikinis Ron 
Published: January 2022

Many neuroanatomy studies rely on brain tissue segmentation in Magnetic Resonance images (MRI). The Expectation-Maximization (EM) theory offers a popular framework for this task. We studied the EM algorithm developed at the Surgical Planning Laboratory (SPL) at Harvard Medical School and implemented in the Slicer3 software. We observed that the segmentation lacks accuracy if the image exhibits some intensity inhomogeneity. Moreover the optimum parameters are challenging to estimate. This document aims at describing our solutions within the context of statistical modeling. Our contributions range from algorithm improvements to novel representations of the statistical distribution model. First we added a bias field correction module and exposed the most significant parameters. Second we proposed a new way to select the distribution of the tissues to be segmented. Finally we designed a set of interactive tools to make the segmentation process easier and more accurate. To validate the new segmentation pipeline, we performed our experiments on MRI data and a clinical expert evaluated our results.The source code developed for this work, i.e. the code of the MRIBiasFieldCorrection and EMSegment modules, is part of Slicer3 version 3.5 and can be downloaded by this command: svn co http://svn.slicer.org/Slicer3/trunk Slicer3
Interactive Image Graph Cut Segmentation
Authors: Doria David , Chen Siqi 
Published: January 2022

This document presents a system to “scribble” on an image to mark foreground and background pixels and then feed these pixels to a graph cuts segmentation technique. The interaction is done using the Visualization Toolkit and the processing is done using the Insight Toolkit. The graph cut function itself is the maxflow-v2.21 algorithm written by Yuri Boykov and Vladimir Kolmogorov. This version of their algorithm is released under a GPL license.The code for this paper can be found here: https://github.com/daviddoria/InteractiveImageGraphCutSegmentation/commit/259cd239169c5ea8c44cc68908886eca10cc8239Updates will be published here: https://github.com/daviddoria/InteractiveImageGraphCutSegmentationNote: You must use the latest git version of VTK as of the date of this publication to avoid compiler errors due to newly added functions.
Conformal Flattening ITK Filter
Authors: Gao Yi , Melonakos John , Melonakos John , Tannenbaum Allen 
Published: January 2022

This paper describes the Insight Toolkit (ITK) Conformal Flattening filter: itkConformalFlatteningFilter. This ITK filter is an implementation of a paper by Sigurd Angenent, et al., “On the Laplace-Beltrami Operator and Brain Surface Flattening”. This filter performs an angle preserving map of any genus zero (i.e. no handles) triangulated mesh to the sphere or, alternatively, to the plane. In other words, any given triangle in the resulting sphere will have the same angles as the corresponding triangle in the original mesh, though their areas may differ. In this paper, we describe our code and provide the user with enough details to reproduce the results which we present in this paper. This filter has a variety of applications including the flattening of brain surfaces, which was the initial motivation for this work.
Importing Contours from DICOM-RT Structure Sets
Authors: Dowling Jason , Malaterre Mathieu , Greer Peter B , Salvado Olivier 
Published: January 2022

This paper presents parsing software for importing contours from DICOM RT structure set files into binary image files. This is a common requirement for transferring expert manual segmentations from radiotherapy treatment planning software into external image analysis environments (such as ITK). We provide an implementation of RT structure set importing and give some example results from prostate cancer radiotherapy planning volumes.Please note that this implementation requires GDCM 2.0 to be installed (this is trivial; however this requirement is the reason for the low automatic testing score by the Insight-Journal Dashboard).Please Note: Updated June 2010 for a couple of minor issues (pls. see comments in .cxx) and to remove the requirement for image template. This code now reads the dcm directory and creates a 3D volume and associated binary structure volumes (all in NIfTI format). Usage: itkDICOMRT DicomDirectory inputDICOMRTImageFile OutputFilePrefixFeb 2013: Please note that this code won’t work with ITK4 and above - instead please see: http://hdl.handle.net/10380/3401 . There is also a new test DICOM-RT directory with that paper.Mar 2013 Now correctly handles holes within structures. Thanks to Sebastian Steger for the code fix.
Automatic Junction Detection for Tubular Structures
Authors: Xiong Guanglei , Xing Lei , Taylor Charles 
Published: January 2022

Junctions of tubular structures (vasculature, trachea, neuron, etc) in medical images are critical for the topology of these structures. Identification of them is helpful in many applications. For example, quantification of geometric vascular features, registration of trachea movement due to respiration, tracing of neuron path. However, manual extraction of junctions can be tedious, time-consuming, and subject to operator bias. In this paper, we propose a novel method to detect them automatically and describe how to implement it in ITK framework. The input is a 2D/3D binary image that can be obtained from any segmentation techniques. The output will be positions of junctions and their sizes. There are only two parameters which need to be set by the user. We provide here the implementation as a ITK class: itk::JunctionDetectionFilter. Please cite the following paper if you are interested in our work. G. Xiong, C. Chen, J. Chen, Y. Xie, and L. Xing, Tracking the Motion Trajectories of Junction Structures in 4D CT Images of the Lung, Vol. 57, No. 15, pp. 4905-4930, Physics in Medicine and Biology, 2012.
A Mean Shift Clustering Implementation for VTK
Authors: Doria David 
Published: January 2022

Mean shift clustering is an excellent technique for clustering points when the number of clusters is not known. We present a implementation (vtkMeanShiftClustering) of the simplest version of the algorithm written in a VTK context.The code is currently hosted at http://github.com/daviddoria/vtkMeanShiftClustering .
A Novel Information-Theoretic Point-Set Measure Based on the Jensen-Havrda-Charvat-Tsallis Divergence
Authors: Tustison Nicholas J. , Awate Suyash , Gee James 
Published: January 2022

A novel point-set registration algorithm was proposed in [6] based on minimization of the Jensen-Shannon divergence. In this contribution, we generalize this Jensen-Shannon divergence point-set measure framework to the Jensen-Havrda-Charvat-Tsallis divergence. This generalization permits a ﬁne-tuning of the actual divergence measure between robustness and speciﬁcity. The principle contribution of this submission is theitk::JensenHavrdaCharvatTsallisPointSetMetric class which is derived from the existing itk::PointSetToPointSetMetric. In addition, we provide other classes with utility that would extend beyond the point-set measure framework that we provide in this paper. This includes a point-set analogue of the itk::ImageFunction, i.e. itk::PointSetFunction. From this class we derive the class itk::ManifoldParzenWindowsPointSetFunction which provides a Parzen windowing scheme for learning the local structure of point-sets. Finally, we include the itk::DecomposeTensorFunction class which wraps the different vnl matrix decomposition schemes for easy use within ITK.
Poisson Editing in ITK
Authors: Doria David 
Published: January 2022

This code provides an implementation of two techniques from ``Poisson Image Editing’’ on ITK images. First, we fill a hole in an image given only the pixel values on the boundary. Second, we copy a patch of an image into another image and make the result as smooth as possible. We also explain how these techniques can be used to reconstruct an image after perform editing operations in the gradient domain.The code is available here: https://github.com/daviddoria/PoissonEditing
An Empirical Optimization to Logistic Classification Model
Authors: Senra Filho Antonio Carlos da Silva 
Published: January 2022

Recently, the scientific community has been proposing several automatic algorithms to biomedical image segmentation procedure, being an interesting and helpful approach to assist both technicians and radiologists in this time-consuming and subjective task. One of these interesting and widely used image segmentation method could be the voxel intensity-based algorithms, e.g. image histogram threshold methods, which have been intensively improved in the past decades. Recently, an interesting approach that gained focus is the logistic classification (LC) for object detection in biomedical images. Even though the general concept behind the LC method is fairly known, the proper method’s optimization still commonly adjusted by hand which naturally adds a level of uncertainty and subjectivity in the general segmentation performance. Therefore, an empirical LC optimization is presented, offering a ITK class that performs the LC parameters optimization based on empirical input data analysis. It is worth mentioning that the LogisticContrastEnhancementImageFilter class showed here is also applied on others computational problems, being briefly explained in this document.
A K-Means++ Clustering Implementation for VTK
Authors: Doria David 
Published: January 2022

K-Means clustering is an excellent technique for clustering points when the number of clusters is known. We present a implementation (vtkKMeanClustering) of the algorithm written in a VTK context. We also implement the K-Means++ initialization method which finds the global optimum much more frequently than a naive/random initialization.The code is currently hosted at http://github.com/daviddoria/KMeansClustering .
Simulated Diffusion-Weighted Imaging for the ITK Masses
Authors: Tustison Nicholas J. , Cook Philip , Avants Brian , Stone James 
Published: January 2022

A recent article by Van Hecke et al. [3] describes a framework for creating simulated diffusion-weighted images (DWI). The methodology allows for modeling intersubject variability, regional pathology, and noise and is quite useful for evaluation purposes. This article describes our ITK implementation of this work which we provide to the community as open source.
Adding Refined Isosurface Rendering and Shadow Mapping to vtkGPUVolumeRayCastMapper
Authors: Herrera Imanol , Buchart Carlos , Borro Diego 
Published: January 2022

In the medical simulation world the use of isosurfaces is a common action, as the information from some sources, e.g. CTs, is very well defined, and the isosurface can be easily set. Additionally, in any simulator shadows are a necessary addition to increase the users immersion as well as its depth perception. Unfortunately, the Visualization Toolkit does not offer these features, and so in this paper a modified vtkGPUVolumeRayCastMapper is presented. This modified version allows volumetric isosurface rendering as well as receiving shadows from polygons using the usual pipeline.
Implementation of a 3D thinning algorithm
Authors: Homann Hanno 
Published: January 2022

ITK currently comes with a 2D binary thinning (skeletonisation) method, but does not support 3D or higher. This paper documents a new itkBinaryThinningImageFilter3D and recommends to rename the current filter as itkBinaryThinningImageFilter2D.The 3D method suggested here is a direct implementation of the work of Lee et al. [2] who have demonstrated that their algorithm can find all deletable surface points at every iteration and is thus very fast. The code was tested on six clinical datasets.
Loopy Belief Propagation on MRFs in ITK
Authors: Doria David 
Published: January 2022

This code provides a base implementation of Loopy Belief Propagation on MRFs in ITK. We use binary image denoising as an example problem to demonstrate this code.This document is intended only to describe the implementation, not the theory. A complete tutorial on loopy belief propagation on MRFs is available here:https://github.com/daviddoria/Tutorials/blob/master/BeliefPropagation/BeliefPropagation.pdf?raw=trueThe code at the time of this submission is here: https://github.com/daviddoria/BinaryDenoising/commit/e8bd152723584c3af7e30b5e529b8a80f2f2ee5cAnd the latest version will be here: https://github.com/daviddoria/BinaryDenoising
Penerapan Metode Pembelajaran Kooperatif Tipe Team Accelerated Instruction (TAI) untuk Meningkatkan Aktivitas dan Prestasi Belajar Bahasa Inggris di Kelas XI IPA 4 SMA Negeri 3 Dompu Tahun Pembelajaran 2019/2020
Authors: Akmal Akmal 
Published: January 2022

Penelitian ini bertujuan untuk mengetahui dampak penerapan metode kooperatif tipe TAI yang dikenakan pada siswa kelas XI IPA 4 SMA Negeri 3 Dompu Semester Ganjil tahun pelajaran 2019/2020 terhadap: 1) aktivitas siswa dalam proses pembelajaran Bahasa Inggris, 2) prestasi belajar Bahasa Inggris dan 3) tanggapan siswa terhadap penerapan metode kooperatif tipe TAI. Untuk mencapai tujuan tersebut, diimplementasikan metode kooperatif tipe TAI terhadap siswa kelas XI IPA 4 SMA Negeri 3 Dompu Semester Ganjil tahun pelajaran 2018/2019 yang terdiri atas 32 orang. Data dikumpulkan menggunakan 1) lembar observasi, 2) tes prestasi belajar dan 3) angket. Selanjutnya data yang terkumpul dalam penelitian ini dianalisis secara deskriptif. Hasil penelitian ini menunjukkan bahwa 1) terjadi peningkatan aktivitas belajar siswa yaitu pada siklus I sebesar 11,16 (cukup aktif) dan pada siklus II mencapai 16,38 (aktif), 2) terjadi peningkatan prestasi belajar siswa yaitu pada siklus I dengan nilai rata-rata kelas sebesar 67,88 dan pada siklus II menjadi 74,69, daya serap meningkat dari siklus I sebesar 67,88% menjadi 74,69% pada siklus II dan persentase ketuntasan meningkat dari siklus I sebesar 81,25% menjadi 90,63% pada siklus II dan 3) tanggapan siswa terhadap metode pembelajaran kooperatif tipe STAD sebesar 38,78 tergolong positif.
Tax Morale Depreciation And Entrepreneurship Practice Outside The Formal Economy Framework: which relation?: Exploratory Study In Sidi-AMMAR Algeria
Authors: Abdellaoui Abderrahime , DJEDDI Tarek 
Published: January 2022

This paper aims to explore the nature of the relationship between the tax morale of Algerian entrepreneurs and their practice of entrepreneurship outside the framework of the formal economy. Given the difficulty of identifying the community of the study accurately and the absence of its database, the researchers relied on non-random sampling - the purposive sample- because they allow the selection of sample members to serve the purposes of the research. Thus, face-to-face interviews were conducted with 22 micro-enterprise owners in Sidi Ameur city in the Wilaya of M'Sila.
The study concluded that the asymmetry between formal institutions (the codified laws and regulations) and informal institutions (Community norms, values) in Algeria has caused a decline in the tax morale of entrepreneurs. This contradiction is due to the increase of corruption in the public sector coincided with the bureaucratic weight, as well as deterioration in the risk of detection and punishment as a result of the corruption of public sector employees.
The added value of this research paper is mainly to highlight the importance of analysis based on institutional theory in explaining the phenomenon of the informal economy, in particular the clarification of the inverse relationship between tax morals and the volume of entrepreneurial activity practiced outside the framework of the formal economy. It has also contributed to providing a modern vision based on analysis of the phenomenon at several levels, rather than relying on traditional bilateral analysis (a formal economy and an informal economy).
As for the recommendations, the study has produced a number of recommendations aimed at reducing the asymmetry between the formal institutions and the informal institutions, which will enhance the tax morale, which in turn will contribute to the integration of entrepreneurial activities outside the framework of the formal economy.
Structural support in Algeria: Does it affect the Entrepreneurial intention of university students
Authors: BOUCIF Sidahmed , TOUAITI Mustapha 
Published: January 2022

This study aims to determine the impact of the structural support on the entrepreneurial intention among university students this is based on the theory of planned behavior; the study was conducted using structural equations modeling on a sample of final year business students at the University of Annaba. Firstly, the results showed that the students have a good attitude about the entrepreneurial and they realize that they are able to control their behavior, these factors impact more positively on their entrepreneurial intention. Finally, with regard to the effect of structural support has been found to have a weak effect on students' attitude and we did not find any impact of this factor on perceived behavior control and there entrepreneurial intention.
The Role of Applying the Principles of Fiscal Governance in Enhancing Fiscal Examination Mechanisms. Case Study of the Fiscal Directorate of Oum El Bouaghi District
Authors: TOUAITIA Tahar , MAAMRI Roumaissa 
Published: January 2022

The purpose of this study is to identify the governance system and its principles, And the extent of commitment in Tax institutions to applying their principles.To help them develop tax inspection mechanisms, To achieve the objectives of the study, a random sample of 46 members of the 50 employees of the Directorate of Taxation of Oum El Bouaghi.The study relied on the descriptive method of analysis in order to provide the theoretical basis for the study and inductive in the applied side to test the impact of applying the principles of governance in the development of tax inspection mechanisms. In this study we rely on the questionnaire tool included the first axis of the principles of governance and the second axis contains the terms of the tax inspection, according to the Likert scale five, and to test the hypotheses of the study were adopted some statistical methods such as simple regression coefficients and alpha Cronbach's coefficient .to measure the sincerity of the study, this study concluded The respondents consider that the tax institution in question is concerned with all the principles of governance because all the principles have achieved significant results.
Accounting Education: a theoretical overview
Authors: REDDA Khadidja , KHALFALLAH Zakarya 
Published: January 2022

The main purpose of this paper is to highlight the Accounting Education as a real asset that must continually evolve in order to best prepare future professionals on accounting for the new requirements imposed by technology and globalization. To do this, a descriptive methodological approach has been deployed to present Accounting Education Standards (IESs), which are developed by the International Accounting Education Standards Board (IAESB). These standards are influencing Accounting Education and training worldwide. The goal of the IESs is to ensure that economic decision makers can rely on the competence of professional accountants regardless of the country where the accountants received their education and training.
Two major lessons emerge from this study. On the one hand, that the organizational structure, globalization and technology impact the skills needs of the accountancy profession and, on the other hand, there is a gap between the training provided and the needs of the profession