CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
Unsupervised detection of fibrosis in microscopy images using fractals and fuzzy c-means clustering
Authors
B. Mandelbrot
D. Shotton
+19 more
D.B. Coultas
E. Cox
G. Izbicki
I. Maglogiannis
J.C. Bezdek
K. Foroutan-pour
K. Shiraishi
K. Shiraishi
K.M. Antoniou
L. Iliadis
M. Chalfie
M. Hussain
M. Masseroli
M. Selman
M. Yagura
P. Bedossa
P. König
S. Inoué
T. Caballero
Publication date
1 January 2012
Publisher
'Springer Science and Business Media LLC'
Doi
Cite
Abstract
The advances in improved fluorescent probes and better cameras in collaboration with the advent of computers in imaging and image analysis, assist the task of diagnosis in many fields of biologic and medical research. In this paper, we introduce a computer-assisted image characterization tool based on a Fuzzy clustering method for the quantification of degree of Idiopathic Pulmonary Fibrosis (IPF) in medical images. The implementation of this algorithmic strategy is very promising concerning the issue of the automated assessment of microscopic images of lung fibrotic regions. © 2012 IFIP International Federation for Information Processing
Similar works
Full text
Available Versions
Crossref
See this paper in CORE
Go to the repository landing page
Download from data provider
info:doi/10.1007%2F978-3-642-3...
Last time updated on 11/12/2019
Archive Ouverte en Sciences de l'Information et de la Communication
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:HAL:hal-01521436v1
Last time updated on 21/11/2017