scholarly journals Image Segmentation Project - Open source software practices class

2007 ◽  
Author(s):  
Amit Mukherjee
Keyword(s):  
Image Segmentation ◽  
Open Access ◽  
Open Source ◽  
Level Set ◽  
Open Source Software ◽  
Active Contour ◽  
Open Data ◽  
Geodesic Active Contour ◽  
Level Set Segmentation ◽  
Segmentation Algorithms

This document describes implementation of two Level-set segmentation algorithms using Insight Toolkit ITK . The algorithms chosen for implementation are are 1) Geodesic Active contour Levelset segmentation and 2) Shape Detection Levelset segmentation. The project is oriented to expose the concepts of Open Data, Open Source and Open Access which form the pillars of open-source software ideology.

FCMLSM Segmentation of Micro-Vessels in Slight Defocused Microscopic Images

2019 ◽  
Vol 23 (6) ◽  
pp. 1073-1079
Author(s):  
Zhongming Luo ◽  
Yu Zhang ◽  
Zixuan Zhou ◽  
Xuan Bi ◽  
Haibin Wu ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Clustering Algorithm ◽  
Evolution Equations ◽  
High Efficiency ◽  
Level Set Function ◽  
Level Set Segmentation ◽  
Segmentation Algorithms ◽  
Fcm Clustering ◽  
Set Function

To address problems relating to microscopic micro-vessel images of living bodies, including poor vessel continuity, blurry boundaries between vessel edges and tissue and uneven field illuminance, and this paper put forward a fuzzy-clustering level-set segmentation algorithm. By this method, pre-treated micro-vessel images were segmented by the fuzzy c-means (FCM) clustering algorithm to obtain original contours of interesting areas in images. By the evolution equations of the improved level set function, accurate segmentation of microscopic micro-vessel images was realized. This method can effectively solve the problem of manual initialization of contours, avoid the sensitivity to initialization and improve the accuracy of level-set segmentation. The experiment results indicate that compared with traditional micro-vessel image segmentation algorithms, this algorithm is of high efficiency, good noise immunity and accurate image segmentation.

scholarly journals A brief introduction to Open Data, Open Source Software and Collective Intelligence for environmental data creators and users

2018 ◽  
Author(s):  
Tomislav Hengl ◽  
Ichsani Wheeler ◽  
Robert A MacMillan
Keyword(s):  
Open Access ◽  
Open Source ◽  
Open Source Software ◽  
Collective Intelligence ◽  
Open Data ◽  
Open Science ◽  
Environmental Data ◽  
Key Aspects

Using the term "Open data" has become a bit of a fashion, but using it without clear specifications is misleading i.e. it can be considered just an empty phrase. Probably even worse is the term "Open Science" — can science be NOT open at all? Are we reinventing something that should be obvious from start? This guide tries to clarify some key aspects of Open Data, Open Source Software and Crowdsourcing using examples of projects and business. It aims at helping you understand and appreciate complexity of Open Data, Open Source software and Open Access publications. It was specifically written for producers and users of environmental data, however, the guide will likely be useful to any data producers and user.

scholarly journals A brief introduction to Open Data, Open Source Software and Collective Intelligence for environmental data creators and users

2018 ◽  
Author(s):  
Tomislav Hengl ◽  
Ichsani Wheeler ◽  
Robert A MacMillan
Keyword(s):  
Open Access ◽  
Open Source ◽  
Open Source Software ◽  
Collective Intelligence ◽  
Open Data ◽  
Open Science ◽  
Environmental Data ◽  
Key Aspects

Using the term "Open data" has become a bit of a fashion, but using it without clear specifications is misleading i.e. it can be considered just an empty phrase. Probably even worse is the term "Open Science" — can science be NOT open at all? Are we reinventing something that should be obvious from start? This guide tries to clarify some key aspects of Open Data, Open Source Software and Crowdsourcing using examples of projects and business. It aims at helping you understand and appreciate complexity of Open Data, Open Source software and Open Access publications. It was specifically written for producers and users of environmental data, however, the guide will likely be useful to any data producers and user.

Non-cooperative Iris Segmentation: A Two-stage Approach

2019 ◽  
Vol 12 ◽  
Author(s):  
M. Rajeev Kumar ◽  
K. Arthi
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Iris Recognition ◽  
Pso Algorithm ◽  
Recognition System ◽  
Iris Segmentation ◽  
Two Stage ◽  
Geodesic Active Contour ◽  
Level Set Segmentation ◽  
Novel Approach

: Recently, segmentation of iris image is the most important process in a robust iris recognition system due to the images captured from non-cooperative environments which introduce occlusions, blur, specular reflections, and off-axis. However, several techniques are developed to overcome these drawbacks in the iris segmentation process; it is still a challenging task to localize the iris texture regions. In this research, an effective two-stage of iris segmentation technique is proposed in a non-cooperative environment. At first, modified Geodesic Active Contour-based level set segmentation with Particle Swarm Optimization (PSO) is employed for iris segmentation. In this, the PSO algorithm is used to minimize the energy of the gradient descent equation in a region-based level set segmentation algorithm. Then, the global threshold-based segmentation is employed for pupil region segmentation. The experiment considered two well-known databases such as UBIRIS.V1 and UBIRIS.V2. The simulation outcomes demonstrate that the proposed novel approach attained more accurate and robust iris segmentation under non-cooperative conditions. Also, the results of the modified Geodesic Active Contour-based level set segmentation with the PSO algorithm attained better results than the conventional segmentation techniques.

scholarly journals Using the R Language to Manage and Show Statistical Information in the Cloud

2018 ◽  
Author(s):  
Pau Fonseca i Casas ◽  
Raül Tormos
Keyword(s):  
Open Access ◽  
Open Source ◽  
Open Source Software ◽  
Open Data ◽  
Statistical Information ◽  
Point Of View ◽  
Cloud Infrastructure ◽  
R Language ◽  
Common Framework ◽  
Statistical Operation

We present a methodology to enable users to interact with statistical information owned by an institution and stored in a cloud infrastructure. Mainly based on R, this approach was developed following the open-data philosophy. Also, since we use R, the implementation is mainly based on open-source software. R gives several advantages from the point of view of data management and acquisition, as it becomes a common framework that can be used to structure the processes involved in any statistical operation. This simplifies the access to the data and enable to use all the power of R in the cloud information. This methodology was applied successfully to develop a tool to manage the data of the Centre d’Estudis d’Opinió, but it can be applied to other institutions to enable open access to its data. The infrastructure also was deployed to a cloud infrastructure, to assure the scalability and a 24/7 access.

scholarly journals A brief introduction to Open Data, Open Source Software and Collective Intelligence for environmental data creators and users

2018 ◽  
Author(s):  
Tomislav Hengl ◽  
Ichsani Wheeler ◽  
Robert A MacMillan
Keyword(s):  
Open Access ◽  
Open Source ◽  
Open Source Software ◽  
Collective Intelligence ◽  
Open Data ◽  
Open Science ◽  
Environmental Data ◽  
Key Aspects

Using the term "Open data" has become a bit of a fashion, but using it without clear specifications is misleading i.e. it can be considered just an empty phrase. Probably even worse is the term "Open Science" — can science be NOT open at all? Are we reinventing something that should be obvious from start? This guide tries to clarify some key aspects of Open Data, Open Source Software and Crowdsourcing using examples of projects and business. It aims at helping you understand and appreciate complexity of Open Data, Open Source software and Open Access publications. It was specifically written for producers and users of environmental data, however, the guide will likely be useful to any data producers and user.

scholarly journals Using Open Source, Open Data, and Civic Technology to Address the COVID-19 Pandemic and Infodemic

2021 ◽  
Author(s):  
Shinji Kobayashi ◽  
Luis Falcón ◽  
Hamish Fraser ◽  
Jørn Braa ◽  
Pamod Amarakoon ◽  
...  
Keyword(s):  
Open Source ◽  
Medical Informatics ◽  
Open Source Software ◽  
Collective Intelligence ◽  
Collaborative Work ◽  
Open Data ◽  
Theme Issue ◽  
Health Organizations ◽  
The World ◽  
Civic Technology

Objectives: The emerging COVID-19 pandemic has caused one of the world’s worst health disasters compounded by social confusion with misinformation, the so-called “Infodemic”. In this paper, we discuss how open technology approaches - including data sharing, visualization, and tooling - can address the COVID-19 pandemic and infodemic. Methods: In response to the call for participation in the 2020 International Medical Informatics Association (IMIA) Yearbook theme issue on Medical Informatics and the Pandemic, the IMIA Open Source Working Group surveyed recent works related to the use of Free/Libre/Open Source Software (FLOSS) for this pandemic. Results: FLOSS health care projects including GNU Health, OpenMRS, DHIS2, and others, have responded from the early phase of this pandemic. Data related to COVID-19 have been published from health organizations all over the world. Civic Technology, and the collaborative work of FLOSS and open data groups were considered to support collective intelligence on approaches to managing the pandemic. Conclusion: FLOSS and open data have been effectively used to contribute to managing the COVID-19 pandemic, and open approaches to collaboration can improve trust in data.

scholarly journals Die Research Collection der ETH Zürich

ABI-Technik ◽  
2018 ◽  
Vol 38 (3) ◽  
pp. 223-233
Author(s):  
Barbara Hirschmann
Keyword(s):  
Open Access ◽  
Open Source ◽  
Open Source Software ◽  
Research Collection

ZusammenfassungIm Sommer 2017 lancierte die ETH-Bibliothek nach rund dreijähriger Projektphase die Research Collection, eine neue Publikationsplattform für die Forschenden an der ETH Zürich. Die Plattform vereint die Funktionen einer Hochschulbibliographie, eines Open-Access-Repository und eines Forschungsdaten-Repository unter einem Dach. Sie wurde auf Basis der Open-Source-Software DSpace implementiert und löste zugleich zwei Vorgängersysteme ab. Heute ist die Research Collection ein zentraler Baustein innerhalb der hochschulweiten Informationsinfrastruktur der ETH Zürich.

Optimal image segmentation protocol for 3D printing of aortic dissection through open-source software

2021 ◽  
Author(s):  
Chia-An Wu ◽  
Andrew Squelch ◽  
Zhonghua Sun
Keyword(s):  
Computed Tomography ◽  
Image Segmentation ◽  
3D Printing ◽  
Aortic Dissection ◽  
Open Source ◽  
Open Source Software ◽  
Type B ◽  
Manual Intervention ◽  
Computed Tomography Images ◽  
Computed Tomography Image

Aim: To determine the optimal image segmentation protocol that minimizes the amount of manual intervention and correction required while extracting 3D model geometries suitable for 3D printing of aortic dissection (AD) using open-source software. Materials & methods: Computed tomography images of two type B AD cases were selected with images segmented using a 3D Slicer to create a hollow model containing the aortic wall and intimal tear. A workflow composed of filters, lumen extraction and outer surface creation was developed. Results & conclusion: The average difference in measurements at 14 anatomical locations between the Standard Tessellation Language file and the computed tomography image for cases 1 and 2 were 0.29 and 0.32 mm, respectively. The workflow for the image segmentation of type B AD was able to produce a high-accuracy 3D-printed model in a short time through open-source software.

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