eprintid: 1455547
rev_number: 30
eprint_status: archive
userid: 608
dir: disk0/01/45/55/47
datestamp: 2014-11-11 12:47:49
lastmod: 2021-10-04 00:53:38
status_changed: 2014-11-11 12:47:49
type: article
metadata_visibility: show
item_issues_count: 0
creators_name: Krylov, V
creators_name: Nelson, J
title: Stochastic extraction of elongated curvilinear structures with applications
ispublished: pub
divisions: UCL
divisions: B04
divisions: C06
keywords: curvilinear structure, conditional random field, line extraction, localized Radon transform, mammogram, palmprint,
road extraction
note: This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
abstract: The automatic extraction of elongated curvilinear structures (CLSs) is an important task in various image processing applications, including numerous remote sensing, and biometrical and medical problems. To address this task, we develop a stochastic approach that relies on a fixed-grid, localized Radon transform for line segment extraction and a conditional random field model to incorporate local interactions and refine the extracted CLSs. We propose several different energy data terms, the appropriate choice of which allows us to process images with different noise and geometry properties. The contribution of this paper is the design of a flexible and robust elongated CLS extraction framework that is comparatively fast due to the use of a fixed-grid configuration and fast deterministic Radon-based line detector. We present several different applications of the developed approach, namely: 1) CLS extraction in mammographic images; 2) road networks extraction from optical remotely sensed images; and 3) line extraction from palmprint images. The experimental results demonstrate that the method is fairly robust to CLS curvature and can accurately extract blurred and low-contrast elongated CLS.
date: 2014-12
official_url: http://dx.doi.org/10.1109/TIP.2014.2363612
vfaculties: VMPS
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_source: crossref
elements_id: 992641
doi: 10.1109/TIP.2014.2363612
lyricists_name: Nelson, James Daniel Bryan
lyricists_id: JDBNE39
full_text_status: public
publication: IEEE Transactions on Image Processing
volume: 23
number: 12
pagerange: 5360 -5373
issn: 1057-7149
citation:        Krylov, V;    Nelson, J;      (2014)    Stochastic extraction of elongated curvilinear structures with applications.                   IEEE Transactions on Image Processing , 23  (12)   5360 -5373.    10.1109/TIP.2014.2363612 <https://doi.org/10.1109/TIP.2014.2363612>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/1455547/2/06924805.pdf