Unsupervised Detection of Fibrosis in Microscopy Images Using Fractals and Fuzzy c-Means Clustering - Artificial Intelligence Applications and Innovations - Part I (AIAI 2012)
Conference Papers Year : 2012

Unsupervised Detection of Fibrosis in Microscopy Images Using Fractals and Fuzzy c-Means Clustering

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.
Fichier principal
Vignette du fichier
978-3-642-33409-2_40_Chapter.pdf (2.51 Mo) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-01521436 , version 1 (11-05-2017)

Licence

Identifiers

Cite

S. K. Tasoulis, Ilias Maglogiannis, V. P. Plagianakos. Unsupervised Detection of Fibrosis in Microscopy Images Using Fractals and Fuzzy c-Means Clustering. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. pp.385-394, ⟨10.1007/978-3-642-33409-2_40⟩. ⟨hal-01521436⟩
191 View
117 Download

Altmetric

Share

More