Change search
ReferencesLink to record
Permanent link

Direct link
Image Analysis for Nail-fold Capillaroscopy
KTH, School of Electrical Engineering (EES).
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 80 credits / 120 HE creditsStudent thesis
Abstract [en]

Detection of diseases in an early stage is very important since it can make the treatment of patients easier, safer and more ecient. For the detection of rheumatic diseases, and even prediction of tendencies towards such diseases, capillaroscopy is becoming an increasingly recognized method. Nail-fold capillaroscopy is a non-invasive imaging technique that is used for analysis of microcirculation abnormalities that may lead todisease like systematic sclerosis, Reynauds phenomenon and others.

The main goal of this master thesis project is to provide new tools and techniques for the analysis of capillaroscopy images from the nail-fold area. Image processing and machine learning techniques are applied to images obtained by digital microscopes, like Mediscope as produced by Optilia Instruments AB, Sollentuna. This thesis oers a novel way for segmentation of capillaries from images as well as (semi)automatic capillary width calculation and automatic annotation of capillaries. These tools provide new insights into the structure of capillaries and also reduce the time required for measurement/annotation of capillaries.

Place, publisher, year, edition, pages
2015. , 55 p.
TRITA-EE, ISSN 1653-5146 ; 2015:58
Keyword [en]
Image analysis, capillaroscopy
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
URN: urn:nbn:se:kth:diva-174868OAI: diva2:859593
Subject / course
Information and Communication Technology
Educational program
Master of Science in Engineering - Information and Communication Technology
2015-08-19, A:367, Osquldas väg 10, Stockholm, 13:00 (English)
Available from: 2015-10-21 Created: 2015-10-07 Last updated: 2015-10-21Bibliographically approved

Open Access in DiVA

fulltext(2450 kB)217 downloads
File information
File name FULLTEXT01.pdfFile size 2450 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Vucic, Vladimir
By organisation
School of Electrical Engineering (EES)
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 217 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 290 hits
ReferencesLink to record
Permanent link

Direct link