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Title:Primerjava segmentacijskih algoritmov na posnetkih zdravih in obolelih očeh
Authors:Krivec, Sandi (Author)
Potočnik, Božidar (Mentor) More about this mentor... New window
Šavc, Martin (Co-mentor)
Files:.pdf MAG_Krivec_Sandi_2018.pdf (3,06 MB)
MD5: DCEF1E640769C7F9ACD4514490147D53
 
Language:Slovenian
Work type:Master's thesis/paper (mb22)
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V tej magistrski nalogi smo se ukvarjali s segmentiranjem človeškega očesa ter prepoz-navanjem bolezni na očesu. Preštudirali in primerjali smo obstoječe algoritme za segmen-tacijo očesa. V praktičnem delu naloge smo razvili lastno hibridno metodo, s pomočjo kate-re detektiramo človeško oko ter prepoznamo bolezni. Naš algoritem in sorodne metode smo preizkusili na treh testnih zbirkah slik s človeškimi očmi, in sicer na lastni zbirki ter na javnih zbirkah IITD in UTIRIS. Algoritme smo primerjali kvalitativno in s pomočjo indeksa Jaccard. Rezultati so pokazali, da s pomočjo naše hibridne metode dosežemo najboljše rezultate. Pri zaznavi zenice smo na lastni podatkovni zbirki dosegli 80 % natančnost, na testnih zbirkah IITD in UTIRIS pa 96 % oziroma 97 % natančnost. Pri zaznavi šarenice smo bili najbolj natančni na zbirki UTIRIS (89 % natančnost), sledita pa lastna zbirka (75 %) ter zbirka IITD (59 %). Eksperimentalno smo potrdili domnevo, da s kombinacijo splo-šnih segmentacijskih postopkov in postopkov na osnovi geometrijskih modelov izboljšamo natančnost segmentacije na posnetkih zdravih in obolelih oči. Bolezni oči smo prepozna-vali le na lastni zbirki. Metrika priklic je variirala od 38 % za bolezen Arcus senilis do 93 % za bolezen miozo. Podobno smo opazili pri metriki točnost, ki je variirala od 60 % (vnetje šarenice), pa vse do 92 % (siva mrena). Preliminarni rezultati potrjujejo uspešnost našega pristopa.
Keywords:napredna obdelava slik, segmentacija očesa, segmentacijski postopki, zaznava bolezni človeškega očesa.
Year of publishing:2018
Publisher:S. Krivec
Source:[Maribor
UDC:004.932:617.7(043.2)
COBISS_ID:21498134 New window
NUK URN:URN:SI:UM:DK:AXTQSPKO
Views:418
Downloads:54
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:25.04.2018

Secondary language

Language:English
Title:Comparison of segmentation algorithms on images of healthy and sick human eyes
Abstract:In this master's thesis we were focused on image segmentation techniques and diseases recognition of the human eye. We have researched and compared existing algorithms for eye segmentation. In the practical part of the master thesis we developed our own hybrid method with the help of which we can detect the human eye and recognize diseases. Our algorithm and related methods were tested on three human eyes image collections: our collection, IITD and UTIRIS. The algorithms were compared qualitatively and with Jaccard index. The results showed that we achieve best results with our hybrid method. At pupil detection we achieved 80% accuracy on our own collection, 96% accuracy on IITD and 97% accuracy on UTIRIS image collection. In detecting the iris, we got the best results on the UTIRIS (89% accuracy), followed by our own image collection (75%) and the IITD (59%). Experimentally, we have confirmed hypothesis that with the combination of general segmentation procedures and procedures based on geometric models we improve the accuracy of image segmentation on healthy and diseased eyes. We tested eye disease recognition procedure only on our own collection. The recall rate varied from 38% for Ar-cus senilis to 93% for the Miosis. Precision varied from 60% (Iritis) up to 92% (Cataract). Preliminary results confirm the success of our approach.
Keywords:image processing, eye segmentation, image segmentation algorithms, detection of eye diseases.


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