Polven nivelrikon vakavuuden arviointi röntgenkuvista ohjaamattoman oppimisen keinoin

Tutkielman tavoitteena oli toteuttaa automaattinen menetelmä polven nivelrikkoon liittyvien muuttujien laskemiseen röntgenkuvista ja selvittää minkälainen klusterointi saadaan aikaan laskettujen muuttujien perusteella. Muuttujat laskettiin reunantunnistukseen perustuvalla menetelmällä ja menetelmää...

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Main Author: Nykänen, Visa
Other Authors: Informaatioteknologian tiedekunta, Faculty of Information Technology, Informaatioteknologia, Information Technology, Jyväskylän yliopisto, University of Jyväskylä
Format: Master's thesis
Language:fin
Published: 2020
Subjects:
Online Access: https://jyx.jyu.fi/handle/123456789/67526
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author Nykänen, Visa
author2 Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä
author_facet Nykänen, Visa Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä Nykänen, Visa Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä
author_sort Nykänen, Visa
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description Tutkielman tavoitteena oli toteuttaa automaattinen menetelmä polven nivelrikkoon liittyvien muuttujien laskemiseen röntgenkuvista ja selvittää minkälainen klusterointi saadaan aikaan laskettujen muuttujien perusteella. Muuttujat laskettiin reunantunnistukseen perustuvalla menetelmällä ja menetelmää arvioitiin tarkastelemalla laskettujen muuttujien yhteyksiä aineistossa oleviin asiantuntijoiden tekemiin nivelrikon vakavuusluokituksiin Osteoarthritis Initiative:n kokoamassa aineistossa. Lopulta menetelmän avulla laskettujen arvojen perusteella tehtiin klusterointi K-means++-algoritmia käyttäen. Klusteroinnin havaittiin yleisestä nivelrikon vakavuuden arviointiin tarkoitetusta luokittelujärjestelmästä KL-luokituksesta poiketen jakavan vakavan nivelrikon mediaaliseen ja lateraaliseen sen perusteella, kummallako puolella nivelrikkoon liittyvät havainnot tehtiin. Lisäksi varhaisen nivelrikon havaittiin jakautuvan kahtia eminentian terävöitymisestä laskettujen muuttujien perusteella. The aim of this thesis was to implement an automatic method for calculating features related to osteoarthritis of the knee from X-ray images and to find out what kind of a clustering can be achieved using the calculated features. The features were calculated using a method based on edge detection and the results were evaluated by reviewing the connections between the calculated variables and severity classifications done by field experts in data collected by Osteoarthritis Initiative. Finally the calculated variables were clustered using the K-means++-algorithm. The clustering was found to divide severe osteoarthritis into two groups based on whether the findings related to osteoarthritis were found on the lateral or the medial side of the knee. Also early osteoarthritis was found to be divided into two groups based on the calculated variables regarding sharpening of the tibial eminence.
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spellingShingle Nykänen, Visa Polven nivelrikon vakavuuden arviointi röntgenkuvista ohjaamattoman oppimisen keinoin röntgenkuva ohjaamaton oppiminen reunantunnistus Tietotekniikka Mathematical Information Technology 602 konenäkö koneoppiminen klusterit polvet nivelrikko
title Polven nivelrikon vakavuuden arviointi röntgenkuvista ohjaamattoman oppimisen keinoin
title_full Polven nivelrikon vakavuuden arviointi röntgenkuvista ohjaamattoman oppimisen keinoin
title_fullStr Polven nivelrikon vakavuuden arviointi röntgenkuvista ohjaamattoman oppimisen keinoin Polven nivelrikon vakavuuden arviointi röntgenkuvista ohjaamattoman oppimisen keinoin
title_full_unstemmed Polven nivelrikon vakavuuden arviointi röntgenkuvista ohjaamattoman oppimisen keinoin Polven nivelrikon vakavuuden arviointi röntgenkuvista ohjaamattoman oppimisen keinoin
title_short Polven nivelrikon vakavuuden arviointi röntgenkuvista ohjaamattoman oppimisen keinoin
title_sort polven nivelrikon vakavuuden arviointi röntgenkuvista ohjaamattoman oppimisen keinoin
title_txtP Polven nivelrikon vakavuuden arviointi röntgenkuvista ohjaamattoman oppimisen keinoin
topic röntgenkuva ohjaamaton oppiminen reunantunnistus Tietotekniikka Mathematical Information Technology 602 konenäkö koneoppiminen klusterit polvet nivelrikko
topic_facet 602 Mathematical Information Technology Tietotekniikka klusterit konenäkö koneoppiminen nivelrikko ohjaamaton oppiminen polvet reunantunnistus röntgenkuva
url https://jyx.jyu.fi/handle/123456789/67526 http://www.urn.fi/URN:NBN:fi:jyu-202001271790
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